Exploring Prompt Engineering: Simplifying AI Outputs with Chain of Thought & Few-Shot Prompting

Join Dan, co-founder of Prompt Hub, as he explores the world of prompt engineering, sharing insights on Chain of Thought prompting, few shot prompting, meta prompting, and more.

1. Dan, co-founder of Prompt Hub, will discuss prompt engineering.
2. Prompt engineering is still important for getting better outputs from language models.
3. The challenge lies in understanding what you want the model to do.
4. A competitive advantage can be gained through the use of unique prompts, architecture, and other elements around the model.
5. Simple solutions are often key when working with language models.
6. Spending time on prompt engineering can lead to simpler management if a solution is found.
7. Chain of Thought (CoT) prompting instructs the model to reason or think about a problem before providing a solution.
8. CoT prompting breaks down problems into sub-problems and offers insight into how the model thinks, which can help with troubleshooting.
9. This method is widely applicable and easy to implement.
10. Reasoning models are now being built with CoT capabilities, reducing the need for explicit prompting.
11. To use CoT with reasoning models, add a reasoning token or instruct the model to think step-by-step before generating the output.
12. Another popular approach is to include few-shot examples of reasoning steps in the prompt.
13. Automatic Chain of Thought and Auto Reason are frameworks that generate reasoning chains with or without few-shot examples.
14. Few-shot prompting involves including examples of what you want the model to mimic or understand about your problem.
15. Meta prompting uses LLMs to create, refine, or improve prompts.
16. Various frameworks and tools are available for meta prompting, with some requiring prior knowledge and others being user-friendly.
17. Reasoning models require different prompting methods compared to other language models.
18. Microsoft's Med-Paper discusses the impact of adding examples in a prompt engineering framework, which can lead to worse performance.
19. DeepMind also found that fewer examples and extended reasoning improve performance.
20. When using reasoning models, use minimal prompting with clear task descriptions and encourage more reasoning for better output.
21. Avoid few-shot prompting when working with reasoning models, as it can hurt performance.
22. Prompt Hub offers free resources, including a substack, blog, and community prompts.
23. Encouraging more reasoning in a model can help improve performance, especially when dealing with complex problems.
24. Few examples (one or two) are sufficient for most models, and they should be diverse to cover various inputs the model might encounter.

Source: AI Engineer via YouTube

❓ What do you think? What are the most effective ways to prompt large language models, and how can we strike a balance between simplicity and nuance in our approach? Feel free to share your thoughts in the comments!